Image recognition based on self-distillation and spatial attention mechanism

Guangxu Zhao (), Zhongguang Sun and Yahui Liu
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Guangxu Zhao: China Coal Technology and Engineering Group Chongqing Research Institute, Chongqing 400039, P. R. China
Zhongguang Sun: China Coal Technology and Engineering Group Chongqing Research Institute, Chongqing 400039, P. R. China†State Key Laboratory of Coal Mine Disaster Prevention and Control, Chongqing 400037, P. R. China‡School of Mines, China University of Mining & Technology, Xuzhou, 221116, P. R. China
Yahui Liu: China Coal Technology and Engineering Group Chongqing Research Institute, Chongqing 400039, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 10, 1-19

Abstract: As society advances, computer vision will play an increasingly crucial role in digital and intelligent transformations. Known as deep learning models, Convolutional Neural Networks (CNNs) have emerged as a key component of computer vision due to their superior performance in automatically detecting image features, handling high-dimensional data and performing large-scale classification tasks. This paper examines the development of CNNs, leveraging the strengths of current mainstream image recognition methods, and proposes a Self-Distillation and Attention-based Convolutional Neural Network (SDACNN) model to further enhance CNN accuracy. Experimental results demonstrate that the proposed model effectively accomplishes image recognition tasks.

Keywords: SDACNN; image recognition; Convolutional Neural Networks; self-distillation; attention mechanism (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S012918312542001X

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